The advantages of dense marker sets for linkage analysis with very large families
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Dense sets of hundreds of thousands of markers have been developed for genome-wide association studies. These marker sets are also beneficial for linkage analysis of large, deep pedigrees containing distantly related cases. It is impossible to analyse jointly all genotypes in large pedigrees using the Lander–Green Algorithm, however, as marker density increases it becomes less crucial to analyse all individuals’ genotypes simultaneously. In this report, an approximate multipoint non-parametric technique is described, where large pedigrees are split into many small pedigrees, each containing just two cases. This technique is demonstrated, using phased data from the International Hapmap Project to simulate sets of 10,000, 50,000 and 250,000 markers, showing that it becomes increasingly accurate as more markers are genotyped. This method allows routine linkage analysis of large families with dense marker sets and represents a more easily applied alternative to Monte Carlo Markov Chain methods.
KeywordsMarkov Chain Monte Carlo Variance Component Analysis International HapMap Project Large Pedigree Markov Chain Monte Carlo Technique
The authors would like to thank Terry Speed for his suggestions during the genesis of this project. RT, SQ, JD and JS are supported by an NHMRC Capacity-Building grant, and JS is also supported by an NHMRC Transitional Institute Grant. JM is an NHMRC CJ Martin Fellow.
- Bureau A, Speed TP, Baird PN (2000) Recovering inheritance information for linkage analysis in large pedigrees by Markov chain Monte Carlo multipoint computations. Am J Hum Genet 67:306Google Scholar
- Chiang AP, Beck JS, Yen HJ, Tayeh MK, Scheetz TE, Swiderski RE, Nishimura DY, Braun TA, Kim KY, Huang J, Elbedour K, Carmi R, Slusarski DC, Casavant TL, Stone EM, Sheffield VC (2006) Homozygosity mapping with SNP arrays identifies TRIM32, an E3 ubiquitin ligase, as a Bardet–Biedl syndrome gene (BBS11). Proc Natl Acad Sci USA 103:6287–6292PubMedCrossRefGoogle Scholar
- Service S, Molina J, Deyoung J, Jawaheer D, Aldana I, Vu T, Bejarano J, Fournier E, Ramirez M, Mathews CA, Davanzo P, Macaya G, Sandkuijl L, Sabatti C, Reus V, Freimer N (2006) Results of a SNP genome screen in a large Costa Rican pedigree segregating for severe bipolar disorder. Am J Med Genet B Neuropsychiatr Genet 141:367–373PubMedGoogle Scholar
- Vierimaa O, Georgitsi M, Lehtonen R, Vahteristo P, Kokko A, Raitila A, Tuppurainen K, Ebeling TM, Salmela PI, Paschke R, Gundogdu S, De Menis E, Makinen MJ, Launonen V, Karhu A, Aaltonen LA (2006) Pituitary adenoma predisposition caused by germline mutations in the AIP gene. Science 312:1228–1230PubMedCrossRefGoogle Scholar
- Wilcox MA, Pugh EW, Zhang H, Zhong X, Levinson DF, Kennedy GC, Wijsman EM (2005) Comparison of single-nucleotide polymorphisms and microsatellite markers for linkage analysis in the COGA and simulated data sets for Genetic Analysis Workshop 14: Presentation Groups 1, 2, and 3. Genet Epidemiol 29 (Suppl 1):S7–S28PubMedCrossRefGoogle Scholar
- Xu J, Zheng SL, Komiya A, Mychaleckyj JC, Isaacs SD, Hu JJ, Sterling D, Lange EM, Hawkins GA, Turner A, Ewing CM, Faith DA, Johnson JR, Suzuki H, Bujnovszky P, Wiley KE, DeMarzo AM, Bova GS, Chang B, Hall MC, McCullough DL, Partin AW, Kassabian VS, Carpten JD, Bailey-Wilson JE, Trent JM, Ohar J, Bleecker ER, Walsh PC, Isaacs WB, Meyers DA (2002) Germline mutations and sequence variants of the macrophage scavenger receptor 1 gene are associated with prostate cancer risk. Nat Genet 32:321–325PubMedCrossRefGoogle Scholar
- Yang XR, Beerman M, Bergen AW, Parry DM, Sheridan E, Liebsch NJ, Kelley MJ, Chanock S, Goldstein AM (2005) Corroboration of a familial chordoma locus on chromosome 7q and evidence of genetic heterogeneity using single nucleotide polymorphisms (SNPs). Int J Cancer 116:487–491PubMedCrossRefGoogle Scholar